Nabil Elshafeey

729 total citations
23 papers, 394 citations indexed

About

Nabil Elshafeey is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Nabil Elshafeey has authored 23 papers receiving a total of 394 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Genetics and 5 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Nabil Elshafeey's work include Radiomics and Machine Learning in Medical Imaging (19 papers), Glioma Diagnosis and Treatment (8 papers) and MRI in cancer diagnosis (6 papers). Nabil Elshafeey is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (19 papers), Glioma Diagnosis and Treatment (8 papers) and MRI in cancer diagnosis (6 papers). Nabil Elshafeey collaborates with scholars based in United States, Switzerland and Australia. Nabil Elshafeey's co-authors include Rivka R. Colen, Pascal O. Zinn, Islam Hassan, Aikaterini Kotrotsou, Gregory N. Fuller, Sara Ahmed, Srishti Abrol, Anand Agarwal, Meng Law and Fanny Morón and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and Cancer Research.

In The Last Decade

Nabil Elshafeey

23 papers receiving 392 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Nabil Elshafeey United States	10	290	151	84	81	73	23	394
Julián Pérez-Beteta Spain	11	305 1.1×	127 0.8×	114 1.4×	59 0.7×	81 1.1×	32	422
Hyemin Um United States	7	306 1.1×	122 0.8×	81 1.0×	74 0.9×	83 1.1×	10	354
Srishti Abrol United States	8	231 0.8×	147 1.0×	86 1.0×	96 1.2×	46 0.6×	12	335
Gang Xiao China	12	325 1.1×	255 1.7×	123 1.5×	121 1.5×	47 0.6×	29	564
Archya Dasgupta India	15	306 1.1×	259 1.7×	160 1.9×	86 1.1×	94 1.3×	75	615
Laura Roccograndi United States	5	175 0.6×	174 1.2×	37 0.4×	58 0.7×	30 0.4×	11	401
Martijn P. A. Starmans Netherlands	12	287 1.0×	56 0.4×	168 2.0×	50 0.6×	63 0.9×	29	401
Ginu Thomas United States	7	264 0.9×	221 1.5×	77 0.9×	31 0.4×	37 0.5×	15	342
Shuaitong Zhang China	12	487 1.7×	279 1.8×	185 2.2×	71 0.9×	60 0.8×	24	617
Ali Nabavizadeh United States	9	176 0.6×	160 1.1×	44 0.5×	30 0.4×	37 0.5×	41	285

Countries citing papers authored by Nabil Elshafeey

Since Specialization

Citations

This map shows the geographic impact of Nabil Elshafeey's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nabil Elshafeey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nabil Elshafeey more than expected).

Fields of papers citing papers by Nabil Elshafeey

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nabil Elshafeey. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nabil Elshafeey. The network helps show where Nabil Elshafeey may publish in the future.

Co-authorship network of co-authors of Nabil Elshafeey

This figure shows the co-authorship network connecting the top 25 collaborators of Nabil Elshafeey. A scholar is included among the top collaborators of Nabil Elshafeey based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nabil Elshafeey. Nabil Elshafeey is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Sharma, Manish, et al.. (2024). Enhancing interpretation assistance by real-time query resolution in BICR of oncology clinical trials by leveraging ChatGPT bots. 6-5. 42–42. 1 indexed citations

Lima, Ernesto A. B. F., Chengyue Wu, Angela M. Jarrett, et al.. (2023). Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting. Journal of Computational Science. 69. 102006–102006. 4 indexed citations

Wu, Chengyue, Angela M. Jarrett, Zijian Zhou, et al.. (2022). MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer. Cancer Research. 82(18). 3394–3404. 45 indexed citations

Gersey, Zachary C., Murat Ak, Ahmed Elakkad, et al.. (2022). Pre-operative MRI radiomics model non-invasively predicts key genomic markers and survival in glioblastoma patients. Journal of Neuro-Oncology. 160(1). 253–263. 18 indexed citations

Mittendorf, Elizabeth A., Nabil Elshafeey, Jennifer K. Litton, et al.. (2022). A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer. European Journal of Radiology. 149. 110220–110220. 23 indexed citations

Zhang, Shu, Gaiane M. Rauch, Beatriz E. Adrada, et al.. (2021). Assessment of Early Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer Using Amide Proton Transfer–weighted Chemical Exchange Saturation Transfer MRI: A Pilot Study. Radiology Imaging Cancer. 3(5). e200155–e200155. 14 indexed citations

Colen, Rivka R., Gabriel O Ologun, Pascal O. Zinn, et al.. (2020). Radiomic signatures to predict response to targeted therapy and immune checkpoint blockade in melanoma patients (pts) on neoadjuvant therapy.. Journal of Clinical Oncology. 38(15_suppl). 10067–10067. 6 indexed citations

Colen, Rivka R., Sara Ahmed, Nabil Elshafeey, et al.. (2020). Radiomics to predict response to pembrolizumab in patients with advanced rare cancers.. Journal of Clinical Oncology. 38(5_suppl). 66–66. 2 indexed citations

Tran, Hai T., Vincent K. Lam, Lingzhi Hong, et al.. (2019). P1.01-98 Outcomes in Advanced NSCLC Patients Treated with 1st Line EGFR-TKI Based on Mutation Detection from Tissue or cfDNA-Based Genomic Sequencing. Journal of Thoracic Oncology. 14(10). S399–S400. 1 indexed citations

10.

Elshafeey, Nabil, Aikaterini Kotrotsou, Islam Hassan, et al.. (2019). Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma. Nature Communications. 10(1). 3170–3170. 112 indexed citations

11.

Zinn, Pascal O., Sanjay K. Singh, Aikaterini Kotrotsou, et al.. (2018). A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models. Clinical Cancer Research. 24(24). 6288–6299. 80 indexed citations

12.

Wahid, Kareem A., Aikaterini Kotrotsou, Srishti Abrol, et al.. (2018). Interrogating machine learning classifiers and dimensionality reduction techniques for radiomic prediction of glioma tumor grade.. Journal of Clinical Oncology. 36(15_suppl). 2031–2031. 1 indexed citations

13.

Colen, Rivka R., Ahmed M. Hassan, Nabil Elshafeey, et al.. (2018). NIMG-03. RADIOMIC TEXTURE ANALYSIS TO PREDICT RESPONSE TO IMMUNOTHERAPY. Neuro-Oncology. 20(suppl_6). vi176–vi176. 1 indexed citations

14.

Zinn, Pascal O., Sanjay K. Singh, Aikaterini Kotrotsou, et al.. (2018). 100 Toward the Co-clinical Glioblastoma Treatment Paradigm—Radiomic Machine Learning Identifies Glioblastoma Gene Expression in Patients and Corresponding Xenograft Tumor Models. Neurosurgery. 65(CN_suppl_1). 80–80. 4 indexed citations

15.

Cachia, David, Nabil Elshafeey, Carlos Kamiya-Matsuoka, et al.. (2017). Radiographic patterns of progression with associated outcomes after bevacizumab therapy in glioblastoma patients. Journal of Neuro-Oncology. 135(1). 75–81. 14 indexed citations

16.

Elshafeey, Nabil, Islam Hassan, Pascal O. Zinn, & Rivka R. Colen. (2017). From K-space to Nucleotide. Topics in Magnetic Resonance Imaging. 26(1). 33–41. 2 indexed citations

17.

Hassan, Islam, Aikaterini Kotrotsou, Kristin Alfaro-Munoz, et al.. (2017). NIMG-28. INCREASED MUTATION BURDEN (HYPERMUTATION) IN GLIOMAS IS ASSOCIATED WITH A UNIQUE RADIOMIC TEXTURE SIGNATURE IN MAGNETIC RESONANCE IMAGING. Neuro-Oncology. 19(suppl_6). vi147–vi148. 2 indexed citations

18.

Elshafeey, Nabil, Aikaterini Kotrotsou, Srishti Abrol, et al.. (2017). Multicenter study to demonstrate radiomic texture features derived from MR perfusion images of pseudoprogression compared to true progression in glioblastoma patients.. Journal of Clinical Oncology. 35(15_suppl). 2016–2016. 6 indexed citations

19.

Kotrotsou, Aikaterini, Srishti Abrol, Ahmed M. Hassan, et al.. (2017). NIMG-29. RADIOMIC ANALYSIS ON APPARENT DIFFUSION COEFFICIENT (ADC) MAPS PREDICTS PLATELET-DERIVED GROWTH FACTOR RECEPTOR ALPHA (PDGFRA) GENE AMPLIFICATION FOR NEWLY DIAGNOSED GLIOBLASTOMA PATIENTS. Neuro-Oncology. 19(suppl_6). vi148–vi148. 1 indexed citations

20.

Colen, Rivka R., Islam Hassan, Nabil Elshafeey, & Pascal O. Zinn. (2016). Shedding Light on the 2016 World Health Organization Classification of Tumors of the Central Nervous System in the Era of Radiomics and Radiogenomics. Magnetic Resonance Imaging Clinics of North America. 24(4). 741–749. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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